Interviewer: Kamales Lardi is a bold and strategic thinker in digital and business transformation. She's listed amongst the top 10 global influencers and thought leaders in digital transformation and top 50 women in tech influencers. Since founding Lardi and Partners Consulting in 2012, Kamales has advised multinational companies across Europe, Asia and Africa.
Her pioneering work champions the human side of technology, emphasising the critical balance between people, processes and tools in transformation. She's also the author of the best-selling book, The Human Side of Digital Business Transformation, an essential guide for business leaders navigating next-generation organisational transformation. But today we're here to talk about her latest book, Artificial Intelligence for Business.
Kamales, welcome. You describe writing Artificial Intelligence for Business as an incredible journey, the culmination of years of experience, collaboration and learning. What was your key motivator for writing the book?
Some of the things that are so critical at this point is really for organisations to understand where they stand in terms of skills and capabilities. So start by conducting a skills gap analysis, right? So capturing the existing capabilities and the existing kind of skill level within the organisation to assess how new technologies like artificial intelligence could be applied within the company and where the gaps lie and how these gaps could be filled.
And so this is going towards the direction of not just understanding the skills towards AI application, but also generally the skills and capabilities overall, because you need to also get a good understanding for where AI is going to replace or take over certain tasks. So this sort of current skill level, current skills analysis needs to be done as a starting point. And then you can start filling the gaps by upskilling or reskilling or newskilling people within the organisation.
And this happens at three levels, the way we conduct it. First of all, there's an organisation wide upskilling, which looks at what kind of knowledge or capabilities are required all across the organisation. And this could be a basic understanding for how AI technologies are going to transform your business or certain business functions.
The second level is really looking at workforce digital upskilling. And this is the basic level of AI capabilities that's required across the organisation. And this could be things like data analytics or data or output.
How do you interact with the output of these AI technologies and how do you understand the data that's being produced, governance capabilities, ethical capabilities, and so on. And then you have the needs-based augmentation, which goes into specific functional areas and specific roles. And what are the upskillings required in those specific areas?
So for someone working in marketing, for example, they might need for their role profile, they might need very specific training and upskilling in order to be able to use a new generative AI technology and so on. So that really deep dives into specific areas of business. Now, it's important to create an environment or to kind of establish an environment of continuous learning within the organisation, just considering the fact that AI as a technology is developing at such a rapid pace.
We're seeing new technologies or new platforms and tools coming out on a weekly basis. So every single time you get used to using a specific platform, you have another company coming out with the next updated version. And a good example of this is just looking at image generation across the board.
Where we were half a year ago and where we are today, there's been such a significant acceleration in what the platforms can do, that it's the upskilling is almost, it's impossible to do a one-time upskilling. So this continuous learning path is so important to encourage people to be curious, to explore and experiment, which sometimes can be quite counterproductive in a corporate environment, right? This exploration experimentation element is not often prioritised.
And so companies can help this process by creating sort of a sandbox environment, a safe sandbox where employees are encouraged to safely explore and experiment tools within the boundaries of the, you know, the governance and compliance perspective of the company. So this allows people to safely explore tools and keep building skills. And another element that's so critical is for companies to set up clear policies and guidelines that communicate the boundaries of AI use, right?
What can be done? What should be avoided? What is important for responsible and safe use of AI in business?
And if I can just clarify here, there's a difference between ethical and safe use, right? And this is so important for organisations to also understand and communicate. Responsible, or sorry, ethical use is really around, should you be using these tools?
Are these the right things to be doing with AI? And safe use is what many companies are addressing within their policies, which is, is it safe to use within their business environment? Are we using it correctly within that business space?
Is it being compliant with the rules and regulations? So it's important to address both of these elements in a distinct way and ensure that people are able to utilise these tools and continuously learn without crossing boundaries that they don't understand, basically.
Interviewer: Staying in the ethical space, you say that ethical considerations in AI should go beyond technical design to encompass broader socio-economic implications, including fostering inclusive economic growth strategies. What exactly are inclusive economic growth strategies? And why do you think they matter?
Kamales Lardi: Well, as I said earlier, AI has become so embedded into almost every sector and every part of our daily lives, right? And we need to ensure that the economic growth benefits are realised for all members of society. And this includes marginalised segments and vulnerable populations, right?
So we're seeing this sort of digital divide or digital gap growing where certain regions and certain sectors or segments of the community, the global community, are rapidly gaining access to technologies that are transformative. So AI solutions that are accelerating the way we work, our capabilities for financial growth, for financial independence, and also our sophistication in terms of access to new ways of thinking and new ways of doing things. And then we're seeing certain regions still lag behind in terms of access.
And this concerns me particularly because you know, the typical approach within the tech industry that I've seen, and I've been in this space for over almost three decades now, the tech industry tends to take this grow now, distribute later approach, right? Where if it's not explicitly and intentionally included within their kind of development and approach and go-to-market approach, the aspects of fairness and equitability are sometimes overlooked, right? There's a prioritisation of profit.
And we're seeing a lot of this happen now with the big tech companies as well. There's this AI race, race to the end that's happening where the big companies are really competing with each other to get the best tool out, to be the first in the market, to be the tool that most organisations or most individuals use. And it's being incorporated into almost everything that they offer at this time.
And so there is, I think, a certain blindness towards this equitable and fair use. And we need to ensure that policies around fair use, data privacy and security, participation and access, sustainable practises, and of course, skill development that's incorporated into all of these AI development and implementation and adoption capabilities across the board. I feel like this is a field that is potentially very lucrative, and many organisations are not prioritising this sort of ethical access.
And so companies need to take a stop and assess for themselves the ethical and safe use of these technologies.
Interviewer: You mention a scenario in the book by K. Fooley, AI 2041, where he describes the emergence of occupational restoration companies funded by both government and employers to help retrain and reallocate people into vocational roles and new job opportunities. Based on where we are now, in the AI journey, might something like this actually become a reality?
Kamales Lardi: I believe so, Nicola. I think there's been such a significant momentum building behind the upskilling and reskilling initiatives. Companies are literally spending millions in terms of making sure that their workforce is upskilled, and there's kind of job roles transformation happening, there's demands for new skills and capabilities.
And, you know, we're seeing AI being leveraged to automate tasks, optimise workflows, enhance productivities. And so the way industries and sectors are operating is shifting dramatically, and the demands for how people work and what they do is also shifting dramatically. And at the same time, we're seeing this across regions, almost every developed nation has these national skills frameworks that's been updated with AI capabilities across regions.
So learning environments, or learning and development environments for the workplace for occupational rehabilitation is underway already. And we're seeing this transition, organisations as well as national frameworks helping people to transition into new forms of employment. So given these shifts that are happening, and all of this has happened literally within the last two to three years, right, the rapid development has been within these short timeframes.
So it's not surprising to envision that this sort of formalised occupational restoration companies could be set up in terms of public-private partnerships that could create policies and, you know, really focus on helping the workforce shift and adapt to new AI-first environments. It's a little bit scary to think of those potentials if it's not approached from a human-centric, prioritised way, that it could be quite detrimental. And I think Kaifuli in his book describes a little bit of the kind of negatives of that environment, but he also ends the scenario in an optimistic way.
So I think it's really important that institutions, organisations, and governments that are focused on this sort of workforce or occupational restoration capabilities needs to take a human-centric and prioritise a human-centric approach.
Interviewer: Yes, because otherwise it could perhaps become a bit dystopian, which is one of the things behind the people calling it a crisis. To conclude, from a regulatory perspective, should we be hopeful that the EU's AI Act will prove to be effective when it comes to compliance and ethics, or do we need to put other frameworks in place to deal with the challenge?
Kamales Lardi: So let me start by maybe prefacing and saying the EU AI Act represents a really comprehensive attempt to regulate the AI industry. And this is so necessary, particularly considering some nations and regions starting to walk back the regulatory requirements for the AI industry. So having something as robust as the EU AI Act in the region that would potentially prioritise the users, the end users of these technologies, is so crucial.
And it comes from really a risk-based oversight approach. So it gives you very robust requirements around governance, human oversight, as well as risk assessments. And it takes a phased approach, a phased implementation approach.
So from banning certain types of use or certain practises to making sure that the high-risk systems are covered or not allowed to be used. So it takes a phased approach that's designed to give companies time to adapt as well. So it's not pushing organisations into an environment where they're not able to apply AI within the region.
But I also believe that the Act has some notable challenges, and I've been quite vocal in sharing these challenges over the last year. The risk-based approach is actually quite difficult to adapt within this very fast-paced AI development industry, right? You're always playing a catch-up game with the technology.
And in certain aspects, I think inadvertently, it may force the consolidation of the market, because smaller players and startups may find it very difficult within this environment or within these regulatory requirements to fulfil their internal governance structures, to set those governance structures up, which can be quite expensive, quite effort and capabilities or skills-intensive. And smaller companies may just not have the capabilities to set this up, to fulfil the regulatory requirements set out by the EU AI Act. And so this might result in consolidation of the market, where you see bigger players maybe buying out or taking over smaller players and their innovative technologies.
And you might actually end up, or we as a region might end up creating monopolies in the market, where the bigger players just own big portions of the sector. And this creates challenges not only in terms of crippling innovation, but it also creates dependencies that we don't want to have in the market. And so this is my view.
I think there's big challenges as big tech companies become overly embedded in the AI field. We're seeing them compete very aggressively with one another, and we're kind of losing the necessary balance that's needed within AI. And there's also challenges around loss of accountability for ethical use of AI in those organisations and in the market in general.
So I feel that it's a good starting point, but there still needs to be additional frameworks in place and additional capabilities to address the dynamic aspect of the market and the potential challenges or limitations that the AI Act could create.
Her pioneering work champions the human side of technology, emphasising the critical balance between people, processes and tools in transformation. She's also the author of the best-selling book, The Human Side of Digital Business Transformation, an essential guide for business leaders navigating next-generation organisational transformation. But today we're here to talk about her latest book, Artificial Intelligence for Business.
Kamales, welcome. You describe writing Artificial Intelligence for Business as an incredible journey, the culmination of years of experience, collaboration and learning. What was your key motivator for writing the book?
Some of the things that are so critical at this point is really for organisations to understand where they stand in terms of skills and capabilities. So start by conducting a skills gap analysis, right? So capturing the existing capabilities and the existing kind of skill level within the organisation to assess how new technologies like artificial intelligence could be applied within the company and where the gaps lie and how these gaps could be filled.
And so this is going towards the direction of not just understanding the skills towards AI application, but also generally the skills and capabilities overall, because you need to also get a good understanding for where AI is going to replace or take over certain tasks. So this sort of current skill level, current skills analysis needs to be done as a starting point. And then you can start filling the gaps by upskilling or reskilling or newskilling people within the organisation.
And this happens at three levels, the way we conduct it. First of all, there's an organisation wide upskilling, which looks at what kind of knowledge or capabilities are required all across the organisation. And this could be a basic understanding for how AI technologies are going to transform your business or certain business functions.
The second level is really looking at workforce digital upskilling. And this is the basic level of AI capabilities that's required across the organisation. And this could be things like data analytics or data or output.
How do you interact with the output of these AI technologies and how do you understand the data that's being produced, governance capabilities, ethical capabilities, and so on. And then you have the needs-based augmentation, which goes into specific functional areas and specific roles. And what are the upskillings required in those specific areas?
So for someone working in marketing, for example, they might need for their role profile, they might need very specific training and upskilling in order to be able to use a new generative AI technology and so on. So that really deep dives into specific areas of business. Now, it's important to create an environment or to kind of establish an environment of continuous learning within the organisation, just considering the fact that AI as a technology is developing at such a rapid pace.
We're seeing new technologies or new platforms and tools coming out on a weekly basis. So every single time you get used to using a specific platform, you have another company coming out with the next updated version. And a good example of this is just looking at image generation across the board.
Where we were half a year ago and where we are today, there's been such a significant acceleration in what the platforms can do, that it's the upskilling is almost, it's impossible to do a one-time upskilling. So this continuous learning path is so important to encourage people to be curious, to explore and experiment, which sometimes can be quite counterproductive in a corporate environment, right? This exploration experimentation element is not often prioritised.
And so companies can help this process by creating sort of a sandbox environment, a safe sandbox where employees are encouraged to safely explore and experiment tools within the boundaries of the, you know, the governance and compliance perspective of the company. So this allows people to safely explore tools and keep building skills. And another element that's so critical is for companies to set up clear policies and guidelines that communicate the boundaries of AI use, right?
What can be done? What should be avoided? What is important for responsible and safe use of AI in business?
And if I can just clarify here, there's a difference between ethical and safe use, right? And this is so important for organisations to also understand and communicate. Responsible, or sorry, ethical use is really around, should you be using these tools?
Are these the right things to be doing with AI? And safe use is what many companies are addressing within their policies, which is, is it safe to use within their business environment? Are we using it correctly within that business space?
Is it being compliant with the rules and regulations? So it's important to address both of these elements in a distinct way and ensure that people are able to utilise these tools and continuously learn without crossing boundaries that they don't understand, basically.
Interviewer: Staying in the ethical space, you say that ethical considerations in AI should go beyond technical design to encompass broader socio-economic implications, including fostering inclusive economic growth strategies. What exactly are inclusive economic growth strategies? And why do you think they matter?
Kamales Lardi: Well, as I said earlier, AI has become so embedded into almost every sector and every part of our daily lives, right? And we need to ensure that the economic growth benefits are realised for all members of society. And this includes marginalised segments and vulnerable populations, right?
So we're seeing this sort of digital divide or digital gap growing where certain regions and certain sectors or segments of the community, the global community, are rapidly gaining access to technologies that are transformative. So AI solutions that are accelerating the way we work, our capabilities for financial growth, for financial independence, and also our sophistication in terms of access to new ways of thinking and new ways of doing things. And then we're seeing certain regions still lag behind in terms of access.
And this concerns me particularly because you know, the typical approach within the tech industry that I've seen, and I've been in this space for over almost three decades now, the tech industry tends to take this grow now, distribute later approach, right? Where if it's not explicitly and intentionally included within their kind of development and approach and go-to-market approach, the aspects of fairness and equitability are sometimes overlooked, right? There's a prioritisation of profit.
And we're seeing a lot of this happen now with the big tech companies as well. There's this AI race, race to the end that's happening where the big companies are really competing with each other to get the best tool out, to be the first in the market, to be the tool that most organisations or most individuals use. And it's being incorporated into almost everything that they offer at this time.
And so there is, I think, a certain blindness towards this equitable and fair use. And we need to ensure that policies around fair use, data privacy and security, participation and access, sustainable practises, and of course, skill development that's incorporated into all of these AI development and implementation and adoption capabilities across the board. I feel like this is a field that is potentially very lucrative, and many organisations are not prioritising this sort of ethical access.
And so companies need to take a stop and assess for themselves the ethical and safe use of these technologies.
Interviewer: You mention a scenario in the book by K. Fooley, AI 2041, where he describes the emergence of occupational restoration companies funded by both government and employers to help retrain and reallocate people into vocational roles and new job opportunities. Based on where we are now, in the AI journey, might something like this actually become a reality?
Kamales Lardi: I believe so, Nicola. I think there's been such a significant momentum building behind the upskilling and reskilling initiatives. Companies are literally spending millions in terms of making sure that their workforce is upskilled, and there's kind of job roles transformation happening, there's demands for new skills and capabilities.
And, you know, we're seeing AI being leveraged to automate tasks, optimise workflows, enhance productivities. And so the way industries and sectors are operating is shifting dramatically, and the demands for how people work and what they do is also shifting dramatically. And at the same time, we're seeing this across regions, almost every developed nation has these national skills frameworks that's been updated with AI capabilities across regions.
So learning environments, or learning and development environments for the workplace for occupational rehabilitation is underway already. And we're seeing this transition, organisations as well as national frameworks helping people to transition into new forms of employment. So given these shifts that are happening, and all of this has happened literally within the last two to three years, right, the rapid development has been within these short timeframes.
So it's not surprising to envision that this sort of formalised occupational restoration companies could be set up in terms of public-private partnerships that could create policies and, you know, really focus on helping the workforce shift and adapt to new AI-first environments. It's a little bit scary to think of those potentials if it's not approached from a human-centric, prioritised way, that it could be quite detrimental. And I think Kaifuli in his book describes a little bit of the kind of negatives of that environment, but he also ends the scenario in an optimistic way.
So I think it's really important that institutions, organisations, and governments that are focused on this sort of workforce or occupational restoration capabilities needs to take a human-centric and prioritise a human-centric approach.
Interviewer: Yes, because otherwise it could perhaps become a bit dystopian, which is one of the things behind the people calling it a crisis. To conclude, from a regulatory perspective, should we be hopeful that the EU's AI Act will prove to be effective when it comes to compliance and ethics, or do we need to put other frameworks in place to deal with the challenge?
Kamales Lardi: So let me start by maybe prefacing and saying the EU AI Act represents a really comprehensive attempt to regulate the AI industry. And this is so necessary, particularly considering some nations and regions starting to walk back the regulatory requirements for the AI industry. So having something as robust as the EU AI Act in the region that would potentially prioritise the users, the end users of these technologies, is so crucial.
And it comes from really a risk-based oversight approach. So it gives you very robust requirements around governance, human oversight, as well as risk assessments. And it takes a phased approach, a phased implementation approach.
So from banning certain types of use or certain practises to making sure that the high-risk systems are covered or not allowed to be used. So it takes a phased approach that's designed to give companies time to adapt as well. So it's not pushing organisations into an environment where they're not able to apply AI within the region.
But I also believe that the Act has some notable challenges, and I've been quite vocal in sharing these challenges over the last year. The risk-based approach is actually quite difficult to adapt within this very fast-paced AI development industry, right? You're always playing a catch-up game with the technology.
And in certain aspects, I think inadvertently, it may force the consolidation of the market, because smaller players and startups may find it very difficult within this environment or within these regulatory requirements to fulfil their internal governance structures, to set those governance structures up, which can be quite expensive, quite effort and capabilities or skills-intensive. And smaller companies may just not have the capabilities to set this up, to fulfil the regulatory requirements set out by the EU AI Act. And so this might result in consolidation of the market, where you see bigger players maybe buying out or taking over smaller players and their innovative technologies.
And you might actually end up, or we as a region might end up creating monopolies in the market, where the bigger players just own big portions of the sector. And this creates challenges not only in terms of crippling innovation, but it also creates dependencies that we don't want to have in the market. And so this is my view.
I think there's big challenges as big tech companies become overly embedded in the AI field. We're seeing them compete very aggressively with one another, and we're kind of losing the necessary balance that's needed within AI. And there's also challenges around loss of accountability for ethical use of AI in those organisations and in the market in general.
So I feel that it's a good starting point, but there still needs to be additional frameworks in place and additional capabilities to address the dynamic aspect of the market and the potential challenges or limitations that the AI Act could create.
